CN117522598A - Block chain financial audit and transaction tracking system - Google Patents
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Abstract
The invention discloses a blockchain financial audit and transaction tracking system, which relates to the technical field of enterprise financial transaction audit. The acquisition module acquires financial statement, registration information and contract data of an enterprise in real time, and establishes an enterprise data set X. The abnormal cost transaction monitoring module monitors real-time financial statements by using the enterprise data model and the historical data, detects abnormal conditions and automatically calculates an abnormal coefficient Yc. This reduces the effort of manual analysis. Meanwhile, the risk threshold is set according to the enterprise credibility xyd, so that the system can be more intelligently adapted to the risk management requirements of different enterprises. The business model monitoring module utilizes the registration information and the contract data to establish an accurate business model for matching the transaction product information. To detect a mismatch transaction condition. This helps auditors to better understand the nature and severity of anomalies.
Description
Technical Field
The invention relates to the technical field of enterprise financial transaction auditing, in particular to a blockchain financial auditing and transaction tracking system.
Background
A financial audit and transaction tracking system is an enterprise-level software or information technology solution that is intended to aid organizations in monitoring, auditing, analyzing, and tracking their financial activities and transactions. The system combines key functions of financial auditing and transaction tracking to provide more comprehensive financial management and compliance support.
Traditional auditing methods are usually periodic batch audits, and may miss sudden anomalies. And traditional audit generally requires a great deal of manual analysis, and manual audit requires auditors to invest a great deal of time and manpower resources to analyze and check financial data one by one. This process is often very cumbersome, resulting in prolonged audit periods. Manual auditing is prone to errors due to human factors. Auditors may be inattentive, tired, or ignore some critical information, resulting in inaccurate audits. Therefore, there is a need to provide a blockchain financial audit and transaction tracking system, one of the essential features of blockchain technology being its non-tamper-resistance. Once transaction records are added to the blockchain, they are permanently stored and cannot be modified or deleted. This ensures that the auditor can trust that the data being audited is authentic and accurate.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a blockchain financial auditing and transaction tracking system to solve the problems proposed by the background technology.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the blockchain financial audit and transaction tracking system comprises an acquisition module, an abnormal cost transaction monitoring module, a business model monitoring module and a blockchain tracking module;
in the process of financial audit of enterprises, collecting real-time financial reports and historical data of a plurality of enterprises through multiple channels by the collecting module, and establishing a first subset; collecting registration information and service range information of enterprises, and establishing a second subset; collecting contract data of enterprises, and establishing a third subset; wherein the first subset, the second subset, and the third subset, corresponding to each enterprise, are collectively organized into one enterprise data set X, labeled X1, X2, X3, X, xn, where n represents the nth number of enterprises;
the abnormal cost transaction monitoring module is used for establishing an enterprise data model, scanning an enterprise data set X, analyzing historical data, acquiring standard deviation between each data point and the mean value of the enterprise, identifying data points far away from the mean value, and setting the upper and lower floating ranges of the normal threshold Z of the enterprise according to the analysis result of the historical data; monitoring real-time financial report data by using an anomaly monitoring algorithm, comparing the real-time financial report data with historical data, drawing a time sequence chart according to transaction data when data points exceed the upper and lower floating ranges of a normal threshold Z, calculating the times Yccs and a difference cz of each time period exceeding the upper and lower floating ranges of the normal threshold Z, and calculating to obtain an anomaly coefficient Yc; and the enterprise credibility xyd is obtained through calculation and analysis through historical data, financial indexes and credit reports; obtaining a rating level result according to enterprise credibility xyd, and setting a first period risk threshold Q1 according to the rating level result; when the anomaly coefficient Yc exceeds a first period risk threshold Q1, generating first early warning information, marking the first early warning information in an enterprise data model, and attaching corresponding timestamp information;
The business model monitoring module is used for analyzing the second subset to obtain the range of business products of the enterprise; matching the third subset, extracting transaction service products in a plurality of contract data of the third subset, and calculating the range of the enterprise service products and the similarity Ppd of the transaction service products in the contract data; when the similarity Ppd is lower than a second similarity threshold Q2, generating second early warning information, marking the second early warning information in an enterprise data model, and attaching corresponding timestamp information;
the block chain tracking module is used for summarizing and counting the first early warning information and the second early warning information and establishing an abnormal schedule report.
Preferably, the acquisition module establishes a database unit, a data cleaning unit and a data integration unit;
the database unit is used for collecting real-time financial statement data, registration information, service range information, contract data and history records of each enterprise from multiple channels; establishing a comprehensive database; the real-time financial statement comprises an asset liability statement, a damage benefit statement, a cash flow statement and a stakeholder equity change statement;
the data cleaning unit is used for cleaning and preprocessing data of the comprehensive database;
The data integration unit is used for extracting enterprise-related data according to the first subset, the second subset and the third subset, and integrating the enterprise-related data into an enterprise data set X.
Preferably, the abnormal cost transaction monitoring module comprises a model building unit, a normal threshold Z setting unit and a transaction monitoring unit;
the modeling unit takes the historical data as sample data after receiving the enterprise data set X, and calculates the mean mu and the standard deviation sigma of the data points in the historical data for each quarter or each annual time period; the mean μ represents the mean over the time period and the standard deviation σ represents a measure of the dispersion of the data points;
setting a normal threshold value Z unit, setting a floating range value above and below the normal threshold value Z, and setting a standard deviation which is 2 times of a mean value mu to construct a floating range; the upper-limit floating range setting value of the normal threshold Z is mu+2σ; the lower floating range value of the normal threshold Z is set to μ -2σ; and automatically adjusting the multiplier value according to specific business requirements and risk preferences;
the transaction monitoring unit is used for analyzing the real-time financial statement data, calculating the standard deviation between each data point and the historical mean value, comparing the standard deviation with the upper and lower floating ranges of the normal threshold Z, if the data point exceeds the floating range, considering that the data point is abnormal, and the data point with the upper floating range value mu+2σ higher than the normal threshold Z belongs to a first abnormal value, and the data point with the lower floating range value mu-2σ lower than the normal threshold Z belongs to a second abnormal value.
When the first outlier and the second outlier are identified, a red marking is performed.
Preferably, the abnormal cost transaction monitoring module further includes an abnormal coefficient Yc calculating unit, where the abnormal coefficient Yc calculating unit is configured to identify a first abnormal value and a second abnormal value, calculate the number of times of the first abnormal value and the second abnormal value in each time period, obtain an abnormal number Yccz value, calculate average differences of a plurality of first abnormal values and second abnormal values exceeding an upper-limit floating range value of a normal threshold value Z, sum and then average to obtain an abnormal average difference Yccz, and fit and calculate the abnormal number Yccz and the abnormal average difference Yccz to obtain an abnormal coefficient Yc; the anomaly coefficient Yc is calculated and generated by the following formula:
wherein DJCZ is expressed as a scoring anomaly difference threshold and is used for judging the anomaly degree; the meaning of the formula is that the higher the value of the anomaly coefficient Yc, the higher the degree of anomaly.
Preferably, the abnormal cost transaction monitoring module further comprises a credit analysis unit for selecting features related to credit from historical data, financial indicators and credit reports; the credit-related features include financial health indicators, historical repayment records and drinking scale features;
Establishing a credit rating model, and extracting and analyzing the extracted credit degree related characteristics to obtain: enterprise liquidity rate LdbL, asset liability rate fzb, profit margin LrL, historical payment overdue times: and the enterprise liquidity rate LdbL, the asset liability rate fzb, the profit margin LrL, the historical repayment overdue times Hk, the annual income Nd of the enterprise, the staff number RS and the total asset value Zzc; hk. After the annual income Nd, the number of staff RS and the total asset value Zzc of the enterprise are subjected to dimensionless treatment, the enterprise credibility xyd is obtained through calculation:
wherein Qygm is expressed as an enterprise-scale evaluation value, C 1 Expressed as a first correction constant, C 2 Expressed as a second correction constant, α, b, d, f, and e are expressed as weight values, and α+b+d+e is not less than 1.0.
Preferably, the level a threshold, the level B threshold and the level C threshold are set according to the value of the enterprise confidence xyd; the enterprise credibility xyd is respectively compared with an A-level threshold, a B-level threshold and a C-level threshold to obtain a rating level result;
and according to the rating level result, the higher the rating level result is, the higher the value set by the first periodic risk threshold value Q1 is, the lower the rating level result is, and the lower the value set by the first periodic risk threshold value Q2 is;
After the first period risk threshold value Q1 is set, the abnormal coefficient Yc is compared with the first risk threshold value Q1, and when the abnormal coefficient Yc exceeds the first period risk threshold value Q1, first early warning information is triggered and generated;
the first early warning information comprises specific time of occurrence of the abnormality, value of an abnormality coefficient Yc and data point characteristics of points related to the abnormality;
marking the first early warning information in an enterprise data model, and setting a marking format as follows: timestamp field + anomaly coefficient Yc field + data point feature description field.
Preferably, the business model monitoring module comprises an enterprise business product range identification unit and a matching contract unit;
the enterprise business product range identifying unit analyzes a second subset, wherein the second subset comprises registration information and business range information of enterprises, the enterprise product range is obtained from the second subset, and the business product range comprises enterprise main products, services and industry fields;
the matching contract unit is used for extracting transaction product information from a plurality of contract data of the third subset, wherein the contract data comprises the transaction product information, and the transaction product information is matched with the enterprise business product range B; obtaining a similarity Ppd; the similarity Ppd calculates cosine similarity between two text strings by using a vocabulary vector through a text similarity calculation method; the cosine similarity calculation formula is as follows:
Ppd＝(A·B)/(‖A‖*‖B‖)
Wherein, A represents the vocabulary vector of the text character string extracting the transaction product information from the contract data, B represents the vocabulary vector of the text character string of the service product range, and represents the dot product of the vector, and A and B represent the lengths of the vectors respectively; the meaning of the formula is that the result of the similarity Ppd is between-1 and 1, which is typically normalized to a range of 0 to 1, where 0 means completely mismatched and 1 means completely matched.
Preferably, the service model monitoring module further comprises a second early warning information generating unit; the second early warning information generating unit sets a second similarity threshold Q2, compares the calculated similarity Ppd with the second similarity threshold Q2, and if the calculated similarity Ppd is lower than the second similarity threshold Q2, triggers generation of second early warning information, wherein the second early warning information has the meaning that products of enterprise transaction are inconsistent with products of the actual operating range of the enterprise transaction and belong to abnormal transaction information, and the second early warning information comprises abnormal occurrence time or time period, the value of the similarity Ppd and contract data information related to low similarity;
and marking the second early warning information in the enterprise data model, and adding timestamp information for indicating the occurrence time and the processing state of the low similarity.
Preferably, the first abnormal value includes a cost value, a business profit value and a personnel compensation value with an upper limit floating range set value μ+2σ higher than a normal threshold Z; the second abnormal value includes a cost value, a business profit value, and a personnel compensation value having a lower-limit floating range setting value μ -2σ lower than the normal threshold value Z.
Preferably, the blockchain tracking module generalizes and counts the specific method of the first early warning information and the second early warning information to:
s1, collecting first early warning information and second early warning information from an enterprise data model, wherein the first early warning information and the second early warning information comprise abnormal occurrence time, values of abnormal coefficients Yc, values of similarity Ppd and relevant data point characteristic information;
s2, summarizing and classifying the collected early warning information; separating the first early warning information from the second early warning information, and establishing an abnormal schedule report, wherein the abnormal schedule comprises the following contents:
the time or time period at which the anomaly occurred;
the abnormal type comprises first early warning information and second early warning information;
the related specific numerical values include the value of the anomaly coefficient Yc or the value of the similarity Ppd;
description of relevant data point characteristics or contract information;
suggested actions or processing steps for anomalies;
S3, generating an abnormal schedule report by utilizing the summarized and counted information, wherein the abnormal schedule report is set to be a structured document, a spreadsheet or an online instrument board for audit staff and decision makers to check and analyze;
s4, updating the abnormal schedule report regularly to reflect the latest audit and tracking results.
(III) beneficial effects
The invention provides a blockchain financial audit and transaction tracking system. The beneficial effects are as follows:
1. the blockchain financial auditing and transaction tracking system acquires financial statement, registration information and contract data of enterprises and historical data in real time through the acquisition module. This makes the audit process more dynamic, enabling quick response to potential anomalies, thereby reducing potential risks. The anomaly cost transaction monitoring module is capable of automatically building an enterprise data model, monitoring real-time financial statement data, and detecting anomalies using an anomaly monitoring algorithm. This helps reduce the amount of manual analysis by the auditor while providing greater accuracy and reliability. By analyzing historical data, financial indicators, and credit reports, the system is able to calculate and analyze enterprise trustworthiness xyd, which helps determine the reputation and trustworthiness of the enterprise. The first periodic risk threshold Q1 is set according to the enterprise confidence xyd to help organizations better manage financial risk. This helps to prevent financial fraud and mitigate risk. The system generates first and second pre-warning information to help the organization identify and address potential problems in time. This helps to reduce losses and improve financial stability. The blockchain tracking module generalizes and counts the first and second early warning information to provide structured reports and information for auditors and decision makers, making them easier to understand and take action. The invention adopts automation and real-time monitoring, and the system reduces the time and resources required by the traditional manual audit. This increases the efficiency of the audit, enabling the auditor to focus more on critical issues.
2. The blockchain financial audit and transaction tracking system, a data integration unit is capable of integrating data from multiple channels into one enterprise data set X. This helps to build a comprehensive view of the data, enabling auditors to view and analyze the information in a unified data environment, making it easier to detect potential anomalies. By automatically collecting, integrating and cleaning data, auditors can save a great deal of time and effort, which reduces the effort of traditional manual data finishing, thereby improving auditing efficiency. Manual data sorting is generally prone to errors, and automated processes of data cleaning and preprocessing reduce the risk of errors, improving data accuracy.
3. The blockchain financial auditing and transaction tracking system provides a quantization index for measuring the degree of abnormality by the abnormality coefficient Yc, and helps auditors to better understand the severity of the problem. The higher the value of the coefficient Yc, the higher the degree of abnormality. This allows an auditor or decision maker to take corresponding actions based on the value of the anomaly coefficient, such as raising an alarm, deploying a detailed audit, or taking risk management measures;
enterprises of different ratings levels will obtain different first period risk thresholds Q1. This means that the system can automatically adjust the sensitivity of the risk alert according to different levels of confidence xyd. For high rated businesses, a higher anomaly coefficient Yc is required to trigger an alarm, while for low rated businesses, a smaller anomaly triggers an alarm. This helps ensure that risk management matches enterprise confidence. Comparing the first periodic risk threshold to the anomaly coefficient helps to improve the accuracy of the alert. This means that the system will be more concerned with high risk enterprises, while for low risk enterprises, unnecessary interventions and alarms may be reduced. The first warning information provides the specific time of occurrence of the abnormality, the value of the abnormality coefficient and the data point characteristics of the abnormality related point. This helps the auditor to understand the nature and severity of the anomaly, thereby better taking action
4. The blockchain financial auditing and transaction tracking system can automatically acquire the product range of an enterprise, including main product, service and industry field, by analyzing the registration information and business range information of the enterprise. This helps build an accurate business model, providing a basis for subsequent contract data matching. The system extracts the transaction product information from the contract data and matches it with the business product scope of the enterprise. The similarity Ppd of the matching results is calculated by a cosine similarity calculation method to determine the degree of association between them. And comparing the similarity of the text strings by adopting a cosine similarity calculation method. This is an effective text similarity measure for assessing the degree of similarity between contract data and enterprise business product areas. The system sets a second similarity threshold Q2 and compares the calculated similarity Ppd to this threshold. If the similarity is lower than Q2, the system generates second early warning information which indicates that the transaction product of the enterprise is not matched with the actual operation range of the enterprise, thereby helping to detect abnormal transaction conditions. The second pre-warning information includes the time or time period at which the abnormality occurs, the value of the similarity Ppd, and contract data information related to the low similarity. This provides detailed information that the auditor needs to learn about the anomalies, helping to better understand the problem. And marking the second early warning information in the enterprise data model, and adding timestamp information to indicate the occurrence time and the processing state of the low similarity condition. This facilitates audit and trace records.
Drawings
FIG. 1 is a block diagram of the blockchain financial audit and transaction tracking system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A financial audit and transaction tracking system is an enterprise-level software or information technology solution that is intended to aid organizations in monitoring, auditing, analyzing, and tracking their financial activities and transactions. The system combines key functions of financial auditing and transaction tracking to provide more comprehensive financial management and compliance support.
Traditional auditing methods are usually periodic batch audits, and may miss sudden anomalies. And traditional audit generally requires a great deal of manual analysis, and manual audit requires auditors to invest a great deal of time and manpower resources to analyze and check financial data one by one. This process is often very cumbersome, resulting in prolonged audit periods. Manual auditing is prone to errors due to human factors. Auditors may be inattentive, tired, or ignore some critical information, resulting in inaccurate audits. Therefore, there is a need to provide a blockchain financial audit and transaction tracking system, one of the essential features of blockchain technology being its non-tamper-resistance. Once transaction records are added to the blockchain, they are permanently stored and cannot be modified or deleted. This ensures that the auditor can trust that the data being audited is authentic and accurate.
Example 1
The invention provides a blockchain financial audit and transaction tracking system, referring to fig. 1, comprising an acquisition module, an abnormal cost transaction monitoring module, a business model monitoring module and a blockchain tracking module;
in the process of financial audit of enterprises, collecting real-time financial reports and historical data of a plurality of enterprises through multiple channels by the collecting module, and establishing a first subset; collecting registration information and service range information of enterprises, and establishing a second subset; collecting contract data of enterprises, and establishing a third subset; wherein the first subset, the second subset, and the third subset, corresponding to each enterprise, are collectively organized into one enterprise data set X, labeled X1, X2, X3, X, xn, where n represents the nth number of enterprises;
the abnormal cost transaction monitoring module is used for establishing an enterprise data model, scanning an enterprise data set X, analyzing historical data, acquiring standard deviation between each data point and the mean value of the enterprise, identifying data points far away from the mean value, and setting the upper and lower floating ranges of the normal threshold Z of the enterprise according to the analysis result of the historical data; monitoring real-time financial report data by using an anomaly monitoring algorithm, comparing the real-time financial report data with historical data, drawing a time sequence chart according to transaction data when data points exceed the upper and lower floating ranges of a normal threshold Z, calculating the times Yccs and a difference cz of each time period exceeding the upper and lower floating ranges of the normal threshold Z, and calculating to obtain an anomaly coefficient Yc; and the enterprise credibility xyd is obtained through calculation and analysis through historical data, financial indexes and credit reports; obtaining a rating level result according to enterprise credibility xyd, and setting a first period risk threshold Q1 according to the rating level result; when the anomaly coefficient Yc exceeds a first period risk threshold Q1, generating first early warning information, marking the first early warning information in an enterprise data model, and attaching corresponding timestamp information;
The business model monitoring module is used for analyzing the second subset to obtain the range of business products of the enterprise; matching the third subset, extracting transaction service products in a plurality of contract data of the third subset, and calculating the range of the enterprise service products and the similarity Ppd of the transaction service products in the contract data; when the similarity Ppd is lower than a second similarity threshold Q2, generating second early warning information, marking the second early warning information in an enterprise data model, and attaching corresponding timestamp information;
the block chain tracking module is used for summarizing and counting the first early warning information and the second early warning information and establishing an abnormal schedule report.
In this embodiment, the system acquires financial statement, registration information, contract data, and history data of the enterprise in real time through the acquisition module. This makes the audit process more dynamic, enabling quick response to potential anomalies, thereby reducing potential risks. The anomaly cost transaction monitoring module is capable of automatically building an enterprise data model, monitoring real-time financial statement data, and detecting anomalies using an anomaly monitoring algorithm. This helps reduce the amount of manual analysis by the auditor while providing greater accuracy and reliability. By analyzing historical data, financial indicators, and credit reports, the system is able to calculate and analyze enterprise trustworthiness xyd, which helps determine the reputation and trustworthiness of the enterprise. The first periodic risk threshold Q1 is set according to the enterprise confidence xyd to help organizations better manage financial risk. This helps to prevent financial fraud and mitigate risk. The system generates first and second pre-warning information to help the organization identify and address potential problems in time. This helps to reduce losses and improve financial stability. The blockchain tracking module generalizes and counts the first and second early warning information to provide structured reports and information for auditors and decision makers, making them easier to understand and take action. The invention adopts automation and real-time monitoring, and the system reduces the time and resources required by the traditional manual audit. This increases the efficiency of the audit, enabling the auditor to focus more on critical issues.
Example 2
In this embodiment, for the explanation in embodiment 1, please refer to fig. 1, specifically, the collection module establishes a database unit, a data cleaning unit, and a data integration unit;
the database unit is used for collecting real-time financial statement data, registration information, service range information, contract data and history records of each enterprise from multiple channels; establishing a comprehensive database; the real-time financial statement comprises an asset liability statement, a damage benefit statement, a cash flow statement and a stakeholder equity change statement; the database unit is responsible for building a comprehensive database, storing data from different sources in a central repository. This provides a single source of data, helping to reduce fragmentation and confusion of data, while making the data easier to access. Collecting real-time financial statement data, registration information, business scope information, contract data and history records helps an auditor to obtain the latest data. This is critical for quick response to potential anomalies and timely action. Integrating data from different data sources allows multidimensional data analysis, and auditors can simultaneously consider multiple factors to assess the financial condition of an enterprise, which helps to more fully understand potential problems.
The data cleaning unit is used for cleaning and preprocessing data of the comprehensive database; : the data cleaning unit can clean errors, repetition, inconsistency or invalid data in the data, thereby improving the data quality. This helps ensure that auditors audit based on reliable data.
The data integration unit is used for extracting enterprise-related data according to the first subset, the second subset and the third subset, and integrating the enterprise-related data into an enterprise data set X.
In this embodiment, the data integration unit can integrate data from a plurality of channels into one enterprise data set X. This helps to build a comprehensive view of the data, enabling auditors to view and analyze the information in a unified data environment, making it easier to detect potential anomalies. By automatically collecting, integrating and cleaning data, auditors can save a great deal of time and effort, which reduces the effort of traditional manual data finishing, thereby improving auditing efficiency. Manual data sorting is generally prone to errors, and automated processes of data cleaning and preprocessing reduce the risk of errors, improving data accuracy.
Example 3
In this embodiment, as explained in embodiment 1, referring to fig. 1, specifically, the abnormal cost transaction monitoring module includes a model building unit, a normal threshold setting unit, and a transaction monitoring unit;
The modeling unit takes the historical data as sample data after receiving the enterprise data set X, and calculates the mean mu and the standard deviation sigma of the data points in the historical data for each quarter or each annual time period; the mean μ represents the mean over the time period and the standard deviation σ represents a measure of the dispersion of the data points;
historical data as sample data:
first, historical data, which should be data representing the normal operating state of an enterprise, is selected as a sample data set.
Setting a normal threshold value Z unit, setting a floating range value above and below the normal threshold value Z, and setting a standard deviation which is 2 times of a mean value mu to construct a floating range; the upper-limit floating range setting value of the normal threshold Z is mu+2σ; the lower floating range value of the normal threshold Z is set to μ -2σ; and automatically adjusting the multiplier value according to specific business requirements and risk preferences;
calculating the mean value and standard deviation:
for each particular time period, for example, quarterly or annually, the mean μ and standard deviation σ of the data points in the historical data are calculated. The mean μ represents the mean over the time period and the standard deviation σ represents a measure of the dispersion of the data points.
Setting a normal threshold Z up-down floating range:
In order to determine the normal threshold Z of an enterprise to float up and down, the following method is used:
typically, the normal threshold may be set near the mean μ, e.g., plus or minus 2 standard deviations of the mean may be selected to construct the floating range, which will include approximately 95% of the data.
Specifically, the upper limit of the normal threshold is set to μ+2σ, and the lower limit is set to μ -2σ. This will cover most normal data points. And this factor is adjusted according to the specific business needs and risk preferences.
The transaction monitoring unit is used for analyzing the real-time financial statement data, calculating the standard deviation between each data point and the historical mean value, comparing the standard deviation with the upper and lower floating ranges of the normal threshold Z, if the data point exceeds the floating range, considering that the data point is abnormal, and the data point with the upper floating range value mu+2σ higher than the normal threshold Z belongs to a first abnormal value, and the data point with the lower floating range value mu-2σ lower than the normal threshold Z belongs to a second abnormal value. The transaction monitoring unit analyzes the real-time financial statement data, calculates the standard deviation between each data point and the historical mean value, and compares the standard deviation with the set up-and-down floating range of the normal threshold Z. This enables the system to monitor transactions in real time and mark outlier data points. When the system identifies abnormal data points, it can mark the points in red, which provides a visual way to quickly identify the problem.
When the first outlier and the second outlier are identified, a red marking is performed.
Specifically, the abnormal cost transaction monitoring module further includes an abnormal coefficient Yc calculating unit, where the abnormal coefficient Yc calculating unit is configured to identify a first abnormal value and a second abnormal value, calculate the number of times of the first abnormal value and the second abnormal value in each time period, obtain an abnormal number Yccz value, calculate average differences of a plurality of first abnormal values and second abnormal values exceeding an upper-limit floating range value of a normal threshold value Z, sum and then average to obtain an abnormal average difference Yccz, and fit and calculate the abnormal number Yccz and the abnormal average difference Yccz to obtain an abnormal coefficient Yc; the anomaly coefficient Yc is calculated and generated by the following formula:
wherein DJCZ is expressed as a scoring anomaly difference threshold and is used for judging the anomaly degree; the meaning of the formula is that the higher the value of the anomaly coefficient Yc, the higher the degree of anomaly. The abnormality coefficient Yc calculation unit calculates an abnormality coefficient Yc by counting the number of abnormalities per time period and the abnormality average difference value. The anomaly coefficient Yc provides a quantitative indicator to measure the degree of anomaly, helping auditors to better understand the severity of the problem. The higher the value of the coefficient Yc, the higher the degree of abnormality. This allows an auditor or decision maker to take corresponding actions, such as raising an alarm, deploying a detailed audit, or taking risk management measures, depending on the value of the anomaly coefficient.
Example 4
This embodiment is explained in embodiment 1, referring to fig. 1, specifically, the abnormal cost transaction monitoring module further includes a credit analysis unit, where the credit analysis unit is configured to select features related to the credit from historical data, financial indexes and credit reports; the credit-related features include financial health indicators, historical repayment records and drinking scale features;
establishing a credit rating model, and extracting and analyzing the extracted credit degree related characteristics to obtain: enterprise liquidity rate LdbL, asset liability rate fzb, profit margin LrL, historical payment overdue times: and the enterprise liquidity rate LdbL, the asset liability rate fzb, the profit margin LrL, the historical repayment overdue times Hk, the annual income Nd of the enterprise, the staff number RS and the total asset value Zzc; hk. After the annual income Nd, the number of staff RS and the total asset value Zzc of the enterprise are subjected to dimensionless treatment, the enterprise credibility xyd is obtained through calculation:
wherein Qygm is expressed as an enterprise-scale evaluation value, C 1 Expressed as a first correction constant, C 2 Expressed as a second correction constant, α, b, d, f, and e are expressed as weight values, and α+b+d+e is not less than 1.0.
In this embodiment, the credit analysis unit provides enterprise credits xyd for each enterprise based on the evaluated features and model calculations. Enterprise confidence xyd may be used to compare the confidence of different enterprises, helping auditors or decision makers to better understand the enterprise's credit risk. Parameters in the model, such as correction constants and weight values, can be adjusted according to specific business requirements and risk preferences, so that the model is suitable for different auditing and business environments.
Example 5
This embodiment is explained in embodiment 4, specifically, the level a threshold, the level B threshold, and the level C threshold are set according to the value of the enterprise confidence xyd; the enterprise credibility xyd is respectively compared with an A-level threshold, a B-level threshold and a C-level threshold to obtain a rating level result;
and according to the rating level result, the higher the rating level result is, the higher the value set by the first periodic risk threshold value Q1 is, the lower the rating level result is, and the lower the value set by the first periodic risk threshold value Q2 is;
after the first period risk threshold value Q1 is set, the abnormal coefficient Yc is compared with the first risk threshold value Q1, and when the abnormal coefficient Yc exceeds the first period risk threshold value Q1, first early warning information is triggered and generated;
the first early warning information comprises specific time of occurrence of the abnormality, value of an abnormality coefficient Yc and data point characteristics of points related to the abnormality;
marking the first early warning information in an enterprise data model, and setting a marking format as follows: timestamp field + anomaly coefficient Yc field + data point feature description field. Field name: each information item may be assigned a meaningful field name to clearly represent the content of the information. Timestamp field: for storing the specific time at which the exception occurred. Commonly named "Timestamp or" abnormal time "alert time. Anomaly coefficient field: for storing the value of the anomaly coefficient Yc. Named as "anomaly coefficient" YcValue or "risk index" RiskIndicator. Data point feature field: for storing data point characteristics of points associated with anomalies. May be a text field containing descriptive information about the data point. Named "data point feature" DataPointFeature. Field value: the value of the field will contain the specific content of the corresponding information.
Timestamp field value: the time at which the abnormality occurs is stored, usually in a date and time format such as "YYYY-MM-DDHH: MM: SS". Anomaly coefficient Yc field value: a specific value of the corresponding anomaly coefficient Yc is stored. Data point characteristic field value: specific descriptive information of the data point characteristics of the points associated with the anomaly is stored.
Fields and values in the example data model:
timestamp field: 2023-10-2814:30:00
Anomaly coefficient field: 3.75
Data point feature field: the outlier was found in the liability statement with a liquidity ratio below 0.8.
Such a tag format is capable of storing and retrieving detailed data related to the first pre-warning information,
in this embodiment, enterprises of different rating levels will obtain different first period risk thresholds Q1. This means that the system can automatically adjust the sensitivity of the risk alert according to different levels of confidence xyd. For high rated businesses, a higher anomaly coefficient Yc is required to trigger an alarm, while for low rated businesses, a smaller anomaly triggers an alarm. This helps ensure that risk management matches enterprise confidence. Comparing the first periodic risk threshold to the anomaly coefficient helps to improve the accuracy of the alert. This means that the system will be more concerned with high risk enterprises, while for low risk enterprises, unnecessary interventions and alarms may be reduced. The first warning information provides the specific time of occurrence of the abnormality, the value of the abnormality coefficient and the data point characteristics of the abnormality related point. This helps the auditor to understand the nature and severity of the anomaly, thereby taking better action. The early warning information is marked in the enterprise data model in a specific marking format, so that the information is easy to search and analyze. This helps to build a traceable audit record.
Example 6
This embodiment is explained in embodiment 1, referring to fig. 1, specifically, the business model monitoring module includes an enterprise business product range identifying unit and a matching contract unit;
the enterprise business product range identifying unit analyzes a second subset, wherein the second subset comprises registration information and business range information of enterprises, the enterprise product range is obtained from the second subset, and the business product range comprises enterprise main products, services and industry fields;
the matching contract unit is used for extracting transaction product information from a plurality of contract data of the third subset, wherein the contract data comprises the transaction product information, and the transaction product information is matched with the enterprise business product range B; obtaining a similarity Ppd; the similarity Ppd calculates cosine similarity between two text strings by using a vocabulary vector through a text similarity calculation method; the cosine similarity calculation formula is as follows:
Ppd＝(A·B)/(‖A‖*‖B‖)
wherein, A represents the vocabulary vector of the text character string extracting the transaction product information from the contract data, B represents the vocabulary vector of the text character string of the service product range, and represents the dot product of the vector, and A and B represent the lengths of the vectors respectively; the meaning of the formula is that the result of the similarity Ppd is between-1 and 1, which is typically normalized to a range of 0 to 1, where 0 means completely mismatched and 1 means completely matched.
Specifically, the service model monitoring module further comprises a second early warning information generating unit; the second early warning information generating unit sets a second similarity threshold Q2, compares the calculated similarity Ppd with the second similarity threshold Q2, and if the calculated similarity Ppd is lower than the second similarity threshold Q2, triggers generation of second early warning information, wherein the second early warning information has the meaning that products of enterprise transaction are inconsistent with products of the actual operating range of the enterprise transaction and belong to abnormal transaction information, and the second early warning information comprises abnormal occurrence time or time period, the value of the similarity Ppd and contract data information related to low similarity;
and marking the second early warning information in the enterprise data model, and adding timestamp information for indicating the occurrence time and the processing state of the low similarity.
In this embodiment, by analyzing the registration information and the business scope information of the enterprise, the system can automatically obtain the product scope of the enterprise, including the main product, service and industry field. This helps build an accurate business model, providing a basis for subsequent contract data matching. The system extracts the transaction product information from the contract data and matches it with the business product scope of the enterprise. The similarity Ppd of the matching results is calculated by a cosine similarity calculation method to determine the degree of association between them. And comparing the similarity of the text strings by adopting a cosine similarity calculation method. This is an effective text similarity measure for assessing the degree of similarity between contract data and enterprise business product areas. The system sets a second similarity threshold Q2 and compares the calculated similarity Ppd to this threshold. If the similarity is lower than Q2, the system generates second early warning information which indicates that the transaction product of the enterprise is not matched with the actual operation range of the enterprise, thereby helping to detect abnormal transaction conditions. The second pre-warning information includes the time or time period at which the abnormality occurs, the value of the similarity Ppd, and contract data information related to the low similarity. This provides detailed information that the auditor needs to learn about the anomalies, helping to better understand the problem. And marking the second early warning information in the enterprise data model, and adding timestamp information to indicate the occurrence time and the processing state of the low similarity condition. This facilitates audit and trace records.
Example 7
This embodiment is the explanation made in embodiment 3, specifically, the first abnormal value includes a cost value, a business profit value, and a personnel compensation value with an upper-limit floating range setting value of μ+2σ higher than a normal threshold Z; the second abnormal value includes a cost value, a business profit value, and a personnel compensation value having a lower-limit floating range setting value μ -2σ lower than the normal threshold value Z. The first abnormal value includes a cost value, a business profit value, and a personnel compensation value of an upper limit floating range (μ+2σ) higher than the normal threshold Z. This means that if the cost, business profit or staff compensation of the enterprise is above the upper range of normal thresholds, they will be marked as a first outlier. Anomalies in these factors may indicate that the business's business conditions in these respects are outside of normal limits and may require inspection. The second abnormal value includes a cost value, a business profit value, and a personnel compensation value of a lower-limit floating range (μ -2σ) lower than the normal threshold Z. If the cost, business profit, or staff compensation of the enterprise is below the lower range of normal thresholds, they will be marked as a second outlier. These anomalies may indicate that the business's business conditions in these respects are below normal and also require inspection.
Example 8
The explanation of the embodiment 1 is that, specifically, the blockchain tracking module generalizes and counts a specific method of the first early warning information and the second early warning information to:
s1, collecting first early warning information and second early warning information from an enterprise data model, wherein the first early warning information and the second early warning information comprise abnormal occurrence time, values of abnormal coefficients Yc, values of similarity Ppd and relevant data point characteristic information;
s2, summarizing and classifying the collected early warning information; separating the first early warning information from the second early warning information, and establishing an abnormal schedule report, wherein the abnormal schedule comprises the following contents:
the time or time period at which the anomaly occurred;
the abnormal type comprises first early warning information and second early warning information;
the related specific numerical values include the value of the anomaly coefficient Yc or the value of the similarity Ppd;
description of relevant data point characteristics or contract information;
suggested actions or processing steps for anomalies;
s3, generating an abnormal schedule report by utilizing the summarized and counted information, wherein the abnormal schedule report is set to be a structured document, a spreadsheet or an online instrument board for audit staff and decision makers to check and analyze;
s4, updating the abnormal schedule report regularly to reflect the latest audit and tracking results.
In this embodiment, the collected early warning information is divided into the first early warning information and the second early warning information, and an abnormal schedule report is established. And generating an abnormal schedule report by using the summarized and classified information. This may be a structured document, spreadsheet, or online dashboard so that auditors and decision makers can view and analyze anomalies. To reflect the latest audit and tracking results, the exception schedule report is updated periodically. This ensures that the decision maker always has up-to-date information to take the necessary actions.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. Block chain financial audit and transaction tracking system, its characterized in that: the system comprises an acquisition module, an abnormal cost transaction monitoring module, a business model monitoring module and a blockchain tracking module;
in the process of financial audit of enterprises, collecting real-time financial reports and historical data of a plurality of enterprises through multiple channels by the collecting module, and establishing a first subset; collecting registration information and service range information of enterprises, and establishing a second subset; collecting contract data of enterprises, and establishing a third subset; wherein the first subset, the second subset, and the third subset, corresponding to each enterprise, are collectively organized into one enterprise data set X, labeled X1, X2, X3, X, xn, where n represents the nth number of enterprises;
The abnormal cost transaction monitoring module is used for establishing an enterprise data model, scanning an enterprise data set X, analyzing historical data, acquiring standard deviation between each data point and the mean value of the enterprise, identifying data points far away from the mean value, and setting the upper and lower floating ranges of the normal threshold Z of the enterprise according to the analysis result of the historical data; monitoring real-time financial report data by using an anomaly monitoring algorithm, comparing the real-time financial report data with historical data, drawing a time sequence chart according to transaction data when data points exceed the upper and lower floating ranges of a normal threshold Z, calculating the times Yccs and a difference cz of each time period exceeding the upper and lower floating ranges of the normal threshold Z, and calculating to obtain an anomaly coefficient Yc; and the enterprise credibility xyd is obtained through calculation and analysis through historical data, financial indexes and credit reports; obtaining a rating level result according to enterprise credibility xyd, and setting a first period risk threshold Q1 according to the rating level result; when the anomaly coefficient Yc exceeds a first period risk threshold Q1, generating first early warning information, marking the first early warning information in an enterprise data model, and attaching corresponding timestamp information;
the business model monitoring module is used for analyzing the second subset to obtain the range of business products of the enterprise; matching the third subset, extracting transaction service products in a plurality of contract data of the third subset, and calculating the range of the enterprise service products and the similarity Ppd of the transaction service products in the contract data; when the similarity Ppd is lower than a second similarity threshold Q2, generating second early warning information, marking the second early warning information in an enterprise data model, and attaching corresponding timestamp information;
The block chain tracking module is used for summarizing and counting the first early warning information and the second early warning information and establishing an abnormal schedule report.
2. The blockchain financial audit and transaction tracking system of claim 1, wherein: the acquisition module establishes a database unit, a data cleaning unit and a data integration unit;
the database unit is used for collecting real-time financial statement data, registration information, service range information, contract data and history records of each enterprise from multiple channels; establishing a comprehensive database; the real-time financial statement comprises an asset liability statement, a damage benefit statement, a cash flow statement and a stakeholder equity change statement;
the data cleaning unit is used for cleaning and preprocessing data of the comprehensive database;
the data integration unit is used for extracting enterprise-related data according to the first subset, the second subset and the third subset, and integrating the enterprise-related data into an enterprise data set X.
3. The blockchain financial audit and transaction tracking system of claim 1, wherein: the abnormal cost transaction monitoring module comprises a model building unit, a normal threshold Z setting unit and a transaction monitoring unit;
the modeling unit takes the historical data as sample data after receiving the enterprise data set X, and calculates the mean mu and the standard deviation sigma of the data points in the historical data for each quarter or each annual time period; the mean μ represents the mean over the time period and the standard deviation σ represents a measure of the dispersion of the data points;
Setting a normal threshold value Z unit, setting a floating range value above and below the normal threshold value Z, and setting a standard deviation which is 2 times of a mean value mu to construct a floating range; the upper-limit floating range setting value of the normal threshold Z is mu+2σ; the lower floating range value of the normal threshold Z is set to μ -2σ; and automatically adjusting the multiplier value according to specific business requirements and risk preferences;
the transaction monitoring unit is used for analyzing the real-time financial statement data, calculating the standard deviation between each data point and the historical mean value, comparing the standard deviation with the upper and lower floating ranges of the normal threshold Z, if the data point exceeds the floating range, considering that the data point is abnormal, and the data point with the upper floating range value mu+2σ higher than the normal threshold Z belongs to a first abnormal value, and the data point with the lower floating range value mu-2σ lower than the normal threshold Z belongs to a second abnormal value.
When the first outlier and the second outlier are identified, a red marking is performed.
4. A blockchain financial audit and transaction tracking system as in claim 3, wherein: the abnormal cost transaction monitoring module further comprises an abnormal coefficient Yc calculating unit, wherein the abnormal coefficient Yc calculating unit is used for identifying a first abnormal value and a second abnormal value, calculating the times of the first abnormal value and the second abnormal value in each time period, obtaining abnormal times Yccz values, calculating average difference values of a plurality of first abnormal values and second abnormal values exceeding the upper-limit floating range value of a normal threshold value Z, summing the average difference values, then carrying out average calculation, obtaining an abnormal average difference value Yccz, carrying out fitting calculation on the abnormal times Yccz and the abnormal average difference value Yccz, and obtaining an abnormal coefficient Yc; the anomaly coefficient Yc is calculated and generated by the following formula:
Wherein DJCZ is expressed as a scoring anomaly difference threshold and is used for judging the anomaly degree; the meaning of the formula is that the higher the value of the anomaly coefficient Yc, the higher the degree of anomaly.
5. The blockchain financial audit and transaction tracking system as in claim 4, wherein: the abnormal cost transaction monitoring module further comprises a credit analysis unit for selecting features related to credit from historical data, financial indicators and credit reports; the credit-related features include financial health indicators, historical repayment records, and business scale features;
establishing a credit rating model, and extracting and analyzing the extracted credit degree related characteristics to obtain: enterprise liquidity rate LdbL, asset liability rate fzb, profit margin LrL, historical payment overdue times: and the enterprise liquidity rate LdbL, the asset liability rate fzb, the profit margin LrL, the historical repayment overdue times Hk, the annual income Nd of the enterprise, the staff number RS and the total asset value Zzc; hk. After the annual income Nd, the number of staff RS and the total asset value Zzc of the enterprise are subjected to dimensionless treatment, the enterprise credibility xyd is obtained through calculation:
wherein Qygm is expressed as an enterprise-scale evaluation value, C 1 Expressed as a first correction constant, C 2 Expressed as a second correction constant, α, b, d, f, and e are expressed as weight values, and α+b+d+e is not less than 1.0.
6. The blockchain financial audit and transaction tracking system of claim 1, wherein: setting an A-level threshold, a B-level threshold and a C-level threshold according to the value of enterprise credibility xyd; the enterprise credibility xyd is respectively compared with an A-level threshold, a B-level threshold and a C-level threshold to obtain a rating level result;
and according to the rating level result, the higher the rating level result is, the higher the value set by the first periodic risk threshold value Q1 is, the lower the rating level result is, and the lower the value set by the first periodic risk threshold value Q2 is;
after the first period risk threshold value Q1 is set, the abnormal coefficient Yc is compared with the first risk threshold value Q1, and when the abnormal coefficient Yc exceeds the first period risk threshold value Q1, first early warning information is triggered and generated;
the first early warning information comprises specific time of occurrence of the abnormality, value of an abnormality coefficient Yc and data point characteristics of points related to the abnormality;
marking the first early warning information in an enterprise data model, and setting a marking format as follows: timestamp field + anomaly coefficient Yc field + data point feature description field.
7. The blockchain financial audit and transaction tracking system of claim 1, wherein: the business model monitoring module comprises an enterprise business product range identifying unit and a matching contract unit;
The enterprise business product range identifying unit analyzes a second subset, wherein the second subset comprises registration information and business range information of enterprises, the enterprise product range is obtained from the second subset, and the business product range comprises enterprise main products, services and industry fields;
the matching contract unit is used for extracting transaction product information from a plurality of contract data of the third subset, wherein the contract data comprises the transaction product information, and the transaction product information is matched with the enterprise business product range B; obtaining a similarity Ppd; the similarity Ppd calculates cosine similarity between two text strings by using a vocabulary vector through a text similarity calculation method; the cosine similarity calculation formula is as follows:
Ppd＝(A·B)/(‖A‖*‖B‖)
wherein, A represents the vocabulary vector of the text character string extracting the transaction product information from the contract data, B represents the vocabulary vector of the text character string of the service product range, and represents the dot product of the vector, and A and B represent the lengths of the vectors respectively; the meaning of the formula is that the result of the similarity Ppd is between-1 and 1, which is typically normalized to a range of 0 to 1, where 0 means completely mismatched and 1 means completely matched.
8. The blockchain financial audit and transaction tracking system of claim 1, wherein: the business model monitoring module further comprises a second early warning information generating unit; the second early warning information generating unit sets a second similarity threshold Q2, compares the calculated similarity Ppd with the second similarity threshold Q2, and if the calculated similarity Ppd is lower than the second similarity threshold Q2, triggers generation of second early warning information, wherein the second early warning information has the meaning that products of enterprise transaction are inconsistent with products of the actual operating range of the enterprise transaction and belong to abnormal transaction information, and the second early warning information comprises abnormal occurrence time or time period, the value of the similarity Ppd and contract data information related to low similarity;
And marking the second early warning information in the enterprise data model, and adding timestamp information for indicating the occurrence time and the processing state of the low similarity.
9. A blockchain financial audit and transaction tracking system as in claim 3, wherein: the first abnormal value comprises a cost value, a service profit value and a personnel compensation value with the upper-limit floating range set value of mu+2sigma higher than a normal threshold Z; the second abnormal value includes a cost value, a business profit value, and a personnel compensation value having a lower-limit floating range setting value μ -2σ lower than the normal threshold value Z.
10. The blockchain financial audit and transaction tracking system of claim 9, wherein: the block chain tracking module is used for summarizing and counting the first early warning information and the second early warning information, and the specific method comprises the following steps:
s1, collecting first early warning information and second early warning information from an enterprise data model, wherein the first early warning information and the second early warning information comprise abnormal occurrence time, values of abnormal coefficients Yc, values of similarity Ppd and relevant data point characteristic information;
s2, summarizing and classifying the collected early warning information; separating the first early warning information from the second early warning information, and establishing an abnormal schedule report, wherein the abnormal schedule comprises the following contents:
The time or time period at which the anomaly occurred;
the abnormal type comprises first early warning information and second early warning information;
the related specific numerical values include the value of the anomaly coefficient Yc or the value of the similarity Ppd;
description of relevant data point characteristics or contract information;
suggested actions or processing steps for anomalies;
s3, generating an abnormal schedule report by utilizing the summarized and counted information, wherein the abnormal schedule report is set to be a structured document, a spreadsheet or an online instrument board for audit staff and decision makers to check and analyze;
s4, updating the abnormal schedule report regularly to reflect the latest audit and tracking results.
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